Overview

Dataset statistics

Number of variables41
Number of observations8522
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory328.0 B

Variable types

Numeric18
Categorical23

Alerts

fr_Al_COO is highly correlated with fr_COO and 1 other fieldsHigh correlation
fr_Al_OH is highly correlated with fr_Al_OH_noTertHigh correlation
fr_Al_OH_noTert is highly correlated with fr_Al_OHHigh correlation
fr_COO is highly correlated with fr_Al_COO and 2 other fieldsHigh correlation
fr_COO2 is highly correlated with fr_Al_COO and 2 other fieldsHigh correlation
fr_C_O is highly correlated with fr_C_O_noCOO and 1 other fieldsHigh correlation
fr_C_O_noCOO is highly correlated with fr_C_O and 2 other fieldsHigh correlation
fr_amide is highly correlated with fr_C_O and 2 other fieldsHigh correlation
fr_ArN is highly correlated with fr_NH2High correlation
fr_Ar_NH is highly correlated with fr_NH1 and 1 other fieldsHigh correlation
fr_NH2 is highly correlated with fr_ArNHigh correlation
fr_Nhpyrrole is highly correlated with fr_Ar_NH and 1 other fieldsHigh correlation
fr_Ar_COO is highly correlated with fr_COO and 1 other fieldsHigh correlation
fr_NH1 is highly correlated with fr_Ar_NH and 1 other fieldsHigh correlation
fr_Ndealkylation1 is highly correlated with fr_C_O_noCOO and 1 other fieldsHigh correlation
fr_N_O is highly skewed (γ1 = 23.02680668) Skewed
df_index has unique values Unique
fr_Al_COO has 8011 (94.0%) zeros Zeros
fr_Al_OH has 7737 (90.8%) zeros Zeros
fr_Al_OH_noTert has 7823 (91.8%) zeros Zeros
fr_Ar_OH has 8098 (95.0%) zeros Zeros
fr_COO has 7839 (92.0%) zeros Zeros
fr_COO2 has 7837 (92.0%) zeros Zeros
fr_C_O has 3445 (40.4%) zeros Zeros
fr_C_O_noCOO has 3859 (45.3%) zeros Zeros
fr_N_O has 8492 (99.6%) zeros Zeros
fr_alkyl_halide has 8095 (95.0%) zeros Zeros
fr_allylic_oxid has 8018 (94.1%) zeros Zeros
fr_amide has 4824 (56.6%) zeros Zeros
fr_aniline has 4762 (55.9%) zeros Zeros
fr_aryl_methyl has 5982 (70.2%) zeros Zeros
fr_benzene has 1533 (18.0%) zeros Zeros
fr_bicyclic has 4524 (53.1%) zeros Zeros
fr_ether has 4596 (53.9%) zeros Zeros

Reproduction

Analysis started2022-11-04 07:15:33.239492
Analysis finished2022-11-04 07:16:38.694353
Duration1 minute and 5.45 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct8522
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6220.515372
Minimum0
Maximum12664
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:38.825461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile591.05
Q13023.25
median6158
Q39418.5
95-th percentile11950.95
Maximum12664
Range12664
Interquartile range (IQR)6395.25

Descriptive statistics

Standard deviation3657.676603
Coefficient of variation (CV)0.5880021805
Kurtosis-1.216095087
Mean6220.515372
Median Absolute Deviation (MAD)3197
Skewness0.0250161244
Sum53011232
Variance13378598.13
MonotonicityNot monotonic
2022-11-04T08:16:39.043908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51241
 
< 0.1%
99061
 
< 0.1%
64211
 
< 0.1%
64021
 
< 0.1%
96611
 
< 0.1%
27441
 
< 0.1%
59491
 
< 0.1%
43091
 
< 0.1%
77601
 
< 0.1%
23171
 
< 0.1%
Other values (8512)8512
99.9%
ValueCountFrequency (%)
01
< 0.1%
21
< 0.1%
31
< 0.1%
81
< 0.1%
101
< 0.1%
121
< 0.1%
141
< 0.1%
151
< 0.1%
171
< 0.1%
181
< 0.1%
ValueCountFrequency (%)
126641
< 0.1%
126631
< 0.1%
126611
< 0.1%
126601
< 0.1%
126591
< 0.1%
126581
< 0.1%
126571
< 0.1%
126561
< 0.1%
126541
< 0.1%
126531
< 0.1%

fr_Al_COO
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07111006806
Minimum0
Maximum5
Zeros8011
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:39.219731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3100074197
Coefficient of variation (CV)4.359543285
Kurtosis46.43452819
Mean0.07111006806
Median Absolute Deviation (MAD)0
Skewness5.776962917
Sum606
Variance0.09610460025
MonotonicityNot monotonic
2022-11-04T08:16:39.355729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
08011
94.0%
1438
 
5.1%
261
 
0.7%
48
 
0.1%
33
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
08011
94.0%
1438
 
5.1%
261
 
0.7%
33
 
< 0.1%
48
 
0.1%
51
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
48
 
0.1%
33
 
< 0.1%
261
 
0.7%
1438
 
5.1%
08011
94.0%

fr_Al_OH
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1378784323
Minimum0
Maximum5
Zeros7737
Zeros (%)90.8%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:39.512328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4918050219
Coefficient of variation (CV)3.566946721
Kurtosis23.03846342
Mean0.1378784323
Median Absolute Deviation (MAD)0
Skewness4.411191209
Sum1175
Variance0.2418721796
MonotonicityNot monotonic
2022-11-04T08:16:39.673684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
07737
90.8%
1497
 
5.8%
2213
 
2.5%
353
 
0.6%
417
 
0.2%
55
 
0.1%
ValueCountFrequency (%)
07737
90.8%
1497
 
5.8%
2213
 
2.5%
353
 
0.6%
417
 
0.2%
55
 
0.1%
ValueCountFrequency (%)
55
 
0.1%
417
 
0.2%
353
 
0.6%
2213
 
2.5%
1497
 
5.8%
07737
90.8%

fr_Al_OH_noTert
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1219197372
Minimum0
Maximum5
Zeros7823
Zeros (%)91.8%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:39.828727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4601634792
Coefficient of variation (CV)3.774314889
Kurtosis25.38504456
Mean0.1219197372
Median Absolute Deviation (MAD)0
Skewness4.630351152
Sum1039
Variance0.2117504275
MonotonicityNot monotonic
2022-11-04T08:16:39.988674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
07823
91.8%
1441
 
5.2%
2196
 
2.3%
346
 
0.5%
412
 
0.1%
54
 
< 0.1%
ValueCountFrequency (%)
07823
91.8%
1441
 
5.2%
2196
 
2.3%
346
 
0.5%
412
 
0.1%
54
 
< 0.1%
ValueCountFrequency (%)
54
 
< 0.1%
412
 
0.1%
346
 
0.5%
2196
 
2.3%
1441
 
5.2%
07823
91.8%

fr_ArN
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8175 
1
 
309
2
 
32
3
 
5
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08175
95.9%
1309
 
3.6%
232
 
0.4%
35
 
0.1%
41
 
< 0.1%

Length

2022-11-04T08:16:40.171805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:40.377028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08175
95.9%
1309
 
3.6%
232
 
0.4%
35
 
0.1%
41
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
08175
95.9%
1309
 
3.6%
232
 
0.4%
35
 
0.1%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08175
95.9%
1309
 
3.6%
232
 
0.4%
35
 
0.1%
41
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08175
95.9%
1309
 
3.6%
232
 
0.4%
35
 
0.1%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08175
95.9%
1309
 
3.6%
232
 
0.4%
35
 
0.1%
41
 
< 0.1%

fr_Ar_COO
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8335 
1
 
161
2
 
23
3
 
2
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08335
97.8%
1161
 
1.9%
223
 
0.3%
32
 
< 0.1%
41
 
< 0.1%

Length

2022-11-04T08:16:40.547689image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:40.742246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08335
97.8%
1161
 
1.9%
223
 
0.3%
32
 
< 0.1%
41
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
08335
97.8%
1161
 
1.9%
223
 
0.3%
32
 
< 0.1%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08335
97.8%
1161
 
1.9%
223
 
0.3%
32
 
< 0.1%
41
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08335
97.8%
1161
 
1.9%
223
 
0.3%
32
 
< 0.1%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08335
97.8%
1161
 
1.9%
223
 
0.3%
32
 
< 0.1%
41
 
< 0.1%

fr_HOCCN
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8479 
1
 
41
2
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08479
99.5%
141
 
0.5%
21
 
< 0.1%
31
 
< 0.1%

Length

2022-11-04T08:16:40.925801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:41.125605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08479
99.5%
141
 
0.5%
21
 
< 0.1%
31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
08479
99.5%
141
 
0.5%
21
 
< 0.1%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08479
99.5%
141
 
0.5%
21
 
< 0.1%
31
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08479
99.5%
141
 
0.5%
21
 
< 0.1%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08479
99.5%
141
 
0.5%
21
 
< 0.1%
31
 
< 0.1%

fr_Ar_OH
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06841117109
Minimum0
Maximum5
Zeros8098
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:41.267289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3361971914
Coefficient of variation (CV)4.914361003
Kurtosis50.67120759
Mean0.06841117109
Median Absolute Deviation (MAD)0
Skewness6.334491027
Sum583
Variance0.1130285515
MonotonicityNot monotonic
2022-11-04T08:16:41.408579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
08098
95.0%
1301
 
3.5%
2100
 
1.2%
312
 
0.1%
49
 
0.1%
52
 
< 0.1%
ValueCountFrequency (%)
08098
95.0%
1301
 
3.5%
2100
 
1.2%
312
 
0.1%
49
 
0.1%
52
 
< 0.1%
ValueCountFrequency (%)
52
 
< 0.1%
49
 
0.1%
312
 
0.1%
2100
 
1.2%
1301
 
3.5%
08098
95.0%

fr_Ar_NH
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
7742 
1
 
720
2
 
54
4
 
3
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07742
90.8%
1720
 
8.4%
254
 
0.6%
43
 
< 0.1%
33
 
< 0.1%

Length

2022-11-04T08:16:41.563056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:41.751376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
07742
90.8%
1720
 
8.4%
254
 
0.6%
43
 
< 0.1%
33
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07742
90.8%
1720
 
8.4%
254
 
0.6%
43
 
< 0.1%
33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07742
90.8%
1720
 
8.4%
254
 
0.6%
43
 
< 0.1%
33
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07742
90.8%
1720
 
8.4%
254
 
0.6%
43
 
< 0.1%
33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07742
90.8%
1720
 
8.4%
254
 
0.6%
43
 
< 0.1%
33
 
< 0.1%

fr_COO
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09657357428
Minimum0
Maximum5
Zeros7839
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:41.883020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.359543052
Coefficient of variation (CV)3.72299622
Kurtosis30.36310473
Mean0.09657357428
Median Absolute Deviation (MAD)0
Skewness4.753597433
Sum823
Variance0.1292712062
MonotonicityNot monotonic
2022-11-04T08:16:42.013062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
07839
92.0%
1570
 
6.7%
297
 
1.1%
49
 
0.1%
36
 
0.1%
51
 
< 0.1%
ValueCountFrequency (%)
07839
92.0%
1570
 
6.7%
297
 
1.1%
36
 
0.1%
49
 
0.1%
51
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
49
 
0.1%
36
 
0.1%
297
 
1.1%
1570
 
6.7%
07839
92.0%

fr_COO2
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09680826097
Minimum0
Maximum5
Zeros7837
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:42.142246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3598062409
Coefficient of variation (CV)3.716689437
Kurtosis30.26253802
Mean0.09680826097
Median Absolute Deviation (MAD)0
Skewness4.744937291
Sum825
Variance0.129460531
MonotonicityNot monotonic
2022-11-04T08:16:42.274349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
07837
92.0%
1572
 
6.7%
297
 
1.1%
49
 
0.1%
36
 
0.1%
51
 
< 0.1%
ValueCountFrequency (%)
07837
92.0%
1572
 
6.7%
297
 
1.1%
36
 
0.1%
49
 
0.1%
51
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
49
 
0.1%
36
 
0.1%
297
 
1.1%
1572
 
6.7%
07837
92.0%

fr_C_O
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8900492842
Minimum0
Maximum6
Zeros3445
Zeros (%)40.4%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:42.409998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9215168518
Coefficient of variation (CV)1.03535486
Kurtosis0.8211137976
Mean0.8900492842
Median Absolute Deviation (MAD)1
Skewness0.9661923124
Sum7585
Variance0.8491933082
MonotonicityNot monotonic
2022-11-04T08:16:42.531869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
03445
40.4%
13146
36.9%
21452
17.0%
3396
 
4.6%
471
 
0.8%
59
 
0.1%
63
 
< 0.1%
ValueCountFrequency (%)
03445
40.4%
13146
36.9%
21452
17.0%
3396
 
4.6%
471
 
0.8%
59
 
0.1%
63
 
< 0.1%
ValueCountFrequency (%)
63
 
< 0.1%
59
 
0.1%
471
 
0.8%
3396
 
4.6%
21452
17.0%
13146
36.9%
03445
40.4%

fr_C_O_noCOO
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7992255339
Minimum0
Maximum6
Zeros3859
Zeros (%)45.3%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:42.665719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8918435503
Coefficient of variation (CV)1.115884706
Kurtosis0.9805873342
Mean0.7992255339
Median Absolute Deviation (MAD)1
Skewness1.06009802
Sum6811
Variance0.7953849183
MonotonicityNot monotonic
2022-11-04T08:16:42.790607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
03859
45.3%
12983
35.0%
21288
 
15.1%
3327
 
3.8%
456
 
0.7%
57
 
0.1%
62
 
< 0.1%
ValueCountFrequency (%)
03859
45.3%
12983
35.0%
21288
 
15.1%
3327
 
3.8%
456
 
0.7%
57
 
0.1%
62
 
< 0.1%
ValueCountFrequency (%)
62
 
< 0.1%
57
 
0.1%
456
 
0.7%
3327
 
3.8%
21288
 
15.1%
12983
35.0%
03859
45.3%

fr_C_S
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8291 
1
 
229
4
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08291
97.3%
1229
 
2.7%
41
 
< 0.1%
21
 
< 0.1%

Length

2022-11-04T08:16:42.944067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:43.115739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08291
97.3%
1229
 
2.7%
41
 
< 0.1%
21
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
08291
97.3%
1229
 
2.7%
41
 
< 0.1%
21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08291
97.3%
1229
 
2.7%
41
 
< 0.1%
21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08291
97.3%
1229
 
2.7%
41
 
< 0.1%
21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08291
97.3%
1229
 
2.7%
41
 
< 0.1%
21
 
< 0.1%

fr_Imine
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8235 
1
 
260
2
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08235
96.6%
1260
 
3.1%
227
 
0.3%

Length

2022-11-04T08:16:43.263173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:43.416955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08235
96.6%
1260
 
3.1%
227
 
0.3%

Most occurring characters

ValueCountFrequency (%)
08235
96.6%
1260
 
3.1%
227
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08235
96.6%
1260
 
3.1%
227
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08235
96.6%
1260
 
3.1%
227
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08235
96.6%
1260
 
3.1%
227
 
0.3%

fr_NH1
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
3892 
1
3469 
2
1018 
3
 
134
4
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row2
5th row0

Common Values

ValueCountFrequency (%)
03892
45.7%
13469
40.7%
21018
 
11.9%
3134
 
1.6%
49
 
0.1%

Length

2022-11-04T08:16:43.536220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:43.687397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
03892
45.7%
13469
40.7%
21018
 
11.9%
3134
 
1.6%
49
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03892
45.7%
13469
40.7%
21018
 
11.9%
3134
 
1.6%
49
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03892
45.7%
13469
40.7%
21018
 
11.9%
3134
 
1.6%
49
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03892
45.7%
13469
40.7%
21018
 
11.9%
3134
 
1.6%
49
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03892
45.7%
13469
40.7%
21018
 
11.9%
3134
 
1.6%
49
 
0.1%

fr_NH2
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
7676 
1
 
711
2
 
107
3
 
22
4
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07676
90.1%
1711
 
8.3%
2107
 
1.3%
322
 
0.3%
46
 
0.1%

Length

2022-11-04T08:16:43.822656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:43.969096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
07676
90.1%
1711
 
8.3%
2107
 
1.3%
322
 
0.3%
46
 
0.1%

Most occurring characters

ValueCountFrequency (%)
07676
90.1%
1711
 
8.3%
2107
 
1.3%
322
 
0.3%
46
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07676
90.1%
1711
 
8.3%
2107
 
1.3%
322
 
0.3%
46
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07676
90.1%
1711
 
8.3%
2107
 
1.3%
322
 
0.3%
46
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07676
90.1%
1711
 
8.3%
2107
 
1.3%
322
 
0.3%
46
 
0.1%

fr_N_O
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.007627317531
Minimum0
Maximum6
Zeros8492
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:44.080261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1422835147
Coefficient of variation (CV)18.65446327
Kurtosis646.8032748
Mean0.007627317531
Median Absolute Deviation (MAD)0
Skewness23.02680668
Sum65
Variance0.02024459856
MonotonicityNot monotonic
2022-11-04T08:16:44.187423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
08492
99.6%
215
 
0.2%
17
 
0.1%
36
 
0.1%
61
 
< 0.1%
41
 
< 0.1%
ValueCountFrequency (%)
08492
99.6%
17
 
0.1%
215
 
0.2%
36
 
0.1%
41
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
41
 
< 0.1%
36
 
0.1%
215
 
0.2%
17
 
0.1%
08492
99.6%

fr_Ndealkylation1
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8118 
1
 
391
2
 
12
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08118
95.3%
1391
 
4.6%
212
 
0.1%
31
 
< 0.1%

Length

2022-11-04T08:16:44.334100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:44.484930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08118
95.3%
1391
 
4.6%
212
 
0.1%
31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
08118
95.3%
1391
 
4.6%
212
 
0.1%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08118
95.3%
1391
 
4.6%
212
 
0.1%
31
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08118
95.3%
1391
 
4.6%
212
 
0.1%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08118
95.3%
1391
 
4.6%
212
 
0.1%
31
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
7440 
1
755 
2
 
327

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
07440
87.3%
1755
 
8.9%
2327
 
3.8%

Length

2022-11-04T08:16:44.610867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:44.748275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
07440
87.3%
1755
 
8.9%
2327
 
3.8%

Most occurring characters

ValueCountFrequency (%)
07440
87.3%
1755
 
8.9%
2327
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07440
87.3%
1755
 
8.9%
2327
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07440
87.3%
1755
 
8.9%
2327
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07440
87.3%
1755
 
8.9%
2327
 
3.8%

fr_Nhpyrrole
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
7742 
1
 
720
2
 
54
4
 
3
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07742
90.8%
1720
 
8.4%
254
 
0.6%
43
 
< 0.1%
33
 
< 0.1%

Length

2022-11-04T08:16:44.869016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:45.033011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
07742
90.8%
1720
 
8.4%
254
 
0.6%
43
 
< 0.1%
33
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07742
90.8%
1720
 
8.4%
254
 
0.6%
43
 
< 0.1%
33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07742
90.8%
1720
 
8.4%
254
 
0.6%
43
 
< 0.1%
33
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07742
90.8%
1720
 
8.4%
254
 
0.6%
43
 
< 0.1%
33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07742
90.8%
1720
 
8.4%
254
 
0.6%
43
 
< 0.1%
33
 
< 0.1%

fr_SH
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8510 
1
 
10
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08510
99.9%
110
 
0.1%
22
 
< 0.1%

Length

2022-11-04T08:16:45.211588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:45.412604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08510
99.9%
110
 
0.1%
22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
08510
99.9%
110
 
0.1%
22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08510
99.9%
110
 
0.1%
22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08510
99.9%
110
 
0.1%
22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08510
99.9%
110
 
0.1%
22
 
< 0.1%

fr_aldehyde
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8500 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08500
99.7%
122
 
0.3%

Length

2022-11-04T08:16:46.046157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:46.188578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08500
99.7%
122
 
0.3%

Most occurring characters

ValueCountFrequency (%)
08500
99.7%
122
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08500
99.7%
122
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08500
99.7%
122
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08500
99.7%
122
 
0.3%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8473 
1
 
48
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08473
99.4%
148
 
0.6%
21
 
< 0.1%

Length

2022-11-04T08:16:46.310909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:46.460808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08473
99.4%
148
 
0.6%
21
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
08473
99.4%
148
 
0.6%
21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08473
99.4%
148
 
0.6%
21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08473
99.4%
148
 
0.6%
21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08473
99.4%
148
 
0.6%
21
 
< 0.1%

fr_alkyl_halide
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1674489556
Minimum0
Maximum12
Zeros8095
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:46.611157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.95
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8039955921
Coefficient of variation (CV)4.801436886
Kurtosis38.42927419
Mean0.1674489556
Median Absolute Deviation (MAD)0
Skewness5.68233856
Sum1427
Variance0.6464089121
MonotonicityNot monotonic
2022-11-04T08:16:46.779928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
08095
95.0%
3286
 
3.4%
671
 
0.8%
139
 
0.5%
222
 
0.3%
44
 
< 0.1%
122
 
< 0.1%
51
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
08095
95.0%
139
 
0.5%
222
 
0.3%
3286
 
3.4%
44
 
< 0.1%
51
 
< 0.1%
671
 
0.8%
71
 
< 0.1%
81
 
< 0.1%
122
 
< 0.1%
ValueCountFrequency (%)
122
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
671
 
0.8%
51
 
< 0.1%
44
 
< 0.1%
3286
 
3.4%
222
 
0.3%
139
 
0.5%
08095
95.0%

fr_allylic_oxid
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09633888759
Minimum0
Maximum6
Zeros8018
Zeros (%)94.1%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:46.907823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.449269612
Coefficient of variation (CV)4.663429517
Kurtosis42.41708284
Mean0.09633888759
Median Absolute Deviation (MAD)0
Skewness6.001525375
Sum821
Variance0.2018431843
MonotonicityNot monotonic
2022-11-04T08:16:47.014803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
08018
94.1%
1309
 
3.6%
2115
 
1.3%
345
 
0.5%
429
 
0.3%
55
 
0.1%
61
 
< 0.1%
ValueCountFrequency (%)
08018
94.1%
1309
 
3.6%
2115
 
1.3%
345
 
0.5%
429
 
0.3%
55
 
0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
55
 
0.1%
429
 
0.3%
345
 
0.5%
2115
 
1.3%
1309
 
3.6%
08018
94.1%

fr_amide
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6084252523
Minimum0
Maximum6
Zeros4824
Zeros (%)56.6%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:47.141314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.828598886
Coefficient of variation (CV)1.361874582
Kurtosis2.09559462
Mean0.6084252523
Median Absolute Deviation (MAD)0
Skewness1.444192169
Sum5185
Variance0.6865761139
MonotonicityNot monotonic
2022-11-04T08:16:47.242187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
04824
56.6%
12578
30.3%
2798
 
9.4%
3286
 
3.4%
429
 
0.3%
55
 
0.1%
62
 
< 0.1%
ValueCountFrequency (%)
04824
56.6%
12578
30.3%
2798
 
9.4%
3286
 
3.4%
429
 
0.3%
55
 
0.1%
62
 
< 0.1%
ValueCountFrequency (%)
62
 
< 0.1%
55
 
0.1%
429
 
0.3%
3286
 
3.4%
2798
 
9.4%
12578
30.3%
04824
56.6%

fr_amidine
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8370 
1
 
141
2
 
10
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08370
98.2%
1141
 
1.7%
210
 
0.1%
31
 
< 0.1%

Length

2022-11-04T08:16:47.366027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:47.508291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08370
98.2%
1141
 
1.7%
210
 
0.1%
31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
08370
98.2%
1141
 
1.7%
210
 
0.1%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08370
98.2%
1141
 
1.7%
210
 
0.1%
31
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08370
98.2%
1141
 
1.7%
210
 
0.1%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08370
98.2%
1141
 
1.7%
210
 
0.1%
31
 
< 0.1%

fr_aniline
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5452945318
Minimum0
Maximum6
Zeros4762
Zeros (%)55.9%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:47.610693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7008148484
Coefficient of variation (CV)1.285204248
Kurtosis1.951784924
Mean0.5452945318
Median Absolute Deviation (MAD)0
Skewness1.264917454
Sum4647
Variance0.4911414518
MonotonicityNot monotonic
2022-11-04T08:16:47.717824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
04762
55.9%
12998
35.2%
2656
 
7.7%
391
 
1.1%
412
 
0.1%
52
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
04762
55.9%
12998
35.2%
2656
 
7.7%
391
 
1.1%
412
 
0.1%
52
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
52
 
< 0.1%
412
 
0.1%
391
 
1.1%
2656
 
7.7%
12998
35.2%
04762
55.9%

fr_aryl_methyl
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4166862239
Minimum0
Maximum6
Zeros5982
Zeros (%)70.2%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:47.833128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7330886649
Coefficient of variation (CV)1.759330217
Kurtosis4.868258294
Mean0.4166862239
Median Absolute Deviation (MAD)0
Skewness1.99327738
Sum3551
Variance0.5374189906
MonotonicityNot monotonic
2022-11-04T08:16:47.937692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
05982
70.2%
11713
 
20.1%
2683
 
8.0%
3118
 
1.4%
417
 
0.2%
65
 
0.1%
54
 
< 0.1%
ValueCountFrequency (%)
05982
70.2%
11713
 
20.1%
2683
 
8.0%
3118
 
1.4%
417
 
0.2%
54
 
< 0.1%
65
 
0.1%
ValueCountFrequency (%)
65
 
0.1%
54
 
< 0.1%
417
 
0.2%
3118
 
1.4%
2683
 
8.0%
11713
 
20.1%
05982
70.2%

fr_azide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8517 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08517
99.9%
15
 
0.1%

Length

2022-11-04T08:16:48.094192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:48.226928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08517
99.9%
15
 
0.1%

Most occurring characters

ValueCountFrequency (%)
08517
99.9%
15
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08517
99.9%
15
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08517
99.9%
15
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08517
99.9%
15
 
0.1%

fr_azo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8477 
1
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08477
99.5%
145
 
0.5%

Length

2022-11-04T08:16:48.341648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:48.485966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08477
99.5%
145
 
0.5%

Most occurring characters

ValueCountFrequency (%)
08477
99.5%
145
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08477
99.5%
145
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08477
99.5%
145
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08477
99.5%
145
 
0.5%

fr_barbitur
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8515 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08515
99.9%
17
 
0.1%

Length

2022-11-04T08:16:48.596680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:48.732714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08515
99.9%
17
 
0.1%

Most occurring characters

ValueCountFrequency (%)
08515
99.9%
17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08515
99.9%
17
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08515
99.9%
17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08515
99.9%
17
 
0.1%

fr_benzene
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.329852147
Minimum0
Maximum6
Zeros1533
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:48.829525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8782060224
Coefficient of variation (CV)0.6603786926
Kurtosis-0.1171408996
Mean1.329852147
Median Absolute Deviation (MAD)1
Skewness0.2502593998
Sum11333
Variance0.7712458178
MonotonicityNot monotonic
2022-11-04T08:16:48.936337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
13368
39.5%
22979
35.0%
01533
18.0%
3567
 
6.7%
470
 
0.8%
54
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
01533
18.0%
13368
39.5%
22979
35.0%
3567
 
6.7%
470
 
0.8%
54
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
54
 
< 0.1%
470
 
0.8%
3567
 
6.7%
22979
35.0%
13368
39.5%
01533
18.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8520 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08520
> 99.9%
12
 
< 0.1%

Length

2022-11-04T08:16:49.062527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:49.198453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08520
> 99.9%
12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
08520
> 99.9%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08520
> 99.9%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08520
> 99.9%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08520
> 99.9%
12
 
< 0.1%

fr_bicyclic
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7514667918
Minimum0
Maximum9
Zeros4524
Zeros (%)53.1%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:49.305505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.111631364
Coefficient of variation (CV)1.479282086
Kurtosis6.423748916
Mean0.7514667918
Median Absolute Deviation (MAD)0
Skewness2.249447587
Sum6404
Variance1.235724289
MonotonicityNot monotonic
2022-11-04T08:16:49.408169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
04524
53.1%
12791
32.8%
2528
 
6.2%
3401
 
4.7%
5122
 
1.4%
4108
 
1.3%
629
 
0.3%
715
 
0.2%
83
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
04524
53.1%
12791
32.8%
2528
 
6.2%
3401
 
4.7%
4108
 
1.3%
5122
 
1.4%
629
 
0.3%
715
 
0.2%
83
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
83
 
< 0.1%
715
 
0.2%
629
 
0.3%
5122
 
1.4%
4108
 
1.3%
3401
 
4.7%
2528
 
6.2%
12791
32.8%
04524
53.1%

fr_diazo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8521 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08521
> 99.9%
11
 
< 0.1%

Length

2022-11-04T08:16:49.535097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:49.665248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08521
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
08521
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08521
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08521
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08521
> 99.9%
11
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8501 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08501
99.8%
121
 
0.2%

Length

2022-11-04T08:16:49.783918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:49.916621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08501
99.8%
121
 
0.2%

Most occurring characters

ValueCountFrequency (%)
08501
99.8%
121
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08501
99.8%
121
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08501
99.8%
121
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08501
99.8%
121
 
0.2%

fr_epoxide
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
8483 
1
 
36
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08483
99.5%
136
 
0.4%
23
 
< 0.1%

Length

2022-11-04T08:16:50.035651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:50.233347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
08483
99.5%
136
 
0.4%
23
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
08483
99.5%
136
 
0.4%
23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08483
99.5%
136
 
0.4%
23
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08483
99.5%
136
 
0.4%
23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08483
99.5%
136
 
0.4%
23
 
< 0.1%

fr_ester
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size66.7 KiB
0
7574 
1
789 
2
 
146
3
 
12
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8522
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07574
88.9%
1789
 
9.3%
2146
 
1.7%
312
 
0.1%
41
 
< 0.1%

Length

2022-11-04T08:16:50.388684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-04T08:16:50.603962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
07574
88.9%
1789
 
9.3%
2146
 
1.7%
312
 
0.1%
41
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07574
88.9%
1789
 
9.3%
2146
 
1.7%
312
 
0.1%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8522
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07574
88.9%
1789
 
9.3%
2146
 
1.7%
312
 
0.1%
41
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07574
88.9%
1789
 
9.3%
2146
 
1.7%
312
 
0.1%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07574
88.9%
1789
 
9.3%
2146
 
1.7%
312
 
0.1%
41
 
< 0.1%

fr_ether
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7033560197
Minimum0
Maximum8
Zeros4596
Zeros (%)53.9%
Negative0
Negative (%)0.0%
Memory size66.7 KiB
2022-11-04T08:16:50.777255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9441686958
Coefficient of variation (CV)1.342376648
Kurtosis3.176717289
Mean0.7033560197
Median Absolute Deviation (MAD)0
Skewness1.593580931
Sum5994
Variance0.8914545262
MonotonicityNot monotonic
2022-11-04T08:16:50.936952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
04596
53.9%
12454
28.8%
21061
 
12.5%
3274
 
3.2%
498
 
1.1%
532
 
0.4%
66
 
0.1%
81
 
< 0.1%
ValueCountFrequency (%)
04596
53.9%
12454
28.8%
21061
 
12.5%
3274
 
3.2%
498
 
1.1%
532
 
0.4%
66
 
0.1%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
66
 
0.1%
532
 
0.4%
498
 
1.1%
3274
 
3.2%
21061
 
12.5%
12454
28.8%
04596
53.9%

Interactions

2022-11-04T08:16:32.828380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:46.853420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:49.172052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:51.483485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:54.080089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:56.353701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:58.610260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:00.910343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:04.196881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:06.698225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:09.112123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:11.309140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:14.005572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:17.082297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:20.013529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:22.924562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:26.076900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:29.708461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:33.001322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:46.977543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:49.300909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:51.612940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:54.220709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:56.481676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:58.745523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:01.026787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:04.320806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:06.832766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:09.230824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:11.440360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:14.134241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:17.298832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:20.158801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:23.089589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:26.241430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:29.895756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:33.170688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:47.104837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:49.438327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:51.733237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:54.346436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:56.615718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:58.867103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:01.149591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:04.468838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:06.951883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:09.342697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:11.565062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:14.254476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:17.493244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:20.312418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:23.252267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:26.384385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:30.057510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:33.340397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:47.267620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:49.571441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:51.851194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:54.470585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:56.750268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:58.989596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:01.276437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:04.595488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:07.063807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:09.459982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:11.695387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:14.391677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:17.649499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:20.454763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:23.415188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:26.527123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:30.235258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:33.550318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:47.410643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:49.713970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:51.985469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:54.590727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:56.882012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:59.126807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:01.394934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:04.716880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:07.209596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:09.581155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:11.818756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:14.527542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:17.791495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:20.602783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:23.579430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:27.017680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:30.432808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:33.747347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:47.537026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:49.841367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:52.126640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:54.710625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:56.997186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:59.243419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:01.534182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:04.840897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:07.361129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:09.692448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:11.944342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:14.668260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:17.930925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:20.751709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:23.731824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:27.181128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:30.615359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:33.921805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:47.657619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:49.971232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:52.254665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:54.827640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:57.109221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:59.369337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:01.650984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:04.957752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:07.514368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:09.822614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:12.069227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:14.805046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:18.124757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:20.890125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:23.904623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:27.344629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:30.790268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:34.093920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:47.785805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:50.112484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:52.406733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:54.939769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:57.230019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:15:59.482721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:01.766815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:05.077898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T08:16:07.673215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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Correlations

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Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-04T08:16:51.809574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-04T08:16:53.068527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-04T08:16:53.601046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-04T08:16:54.102432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-04T08:16:36.472706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-04T08:16:38.314533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexfr_Al_COOfr_Al_OHfr_Al_OH_noTertfr_ArNfr_Ar_COOfr_HOCCNfr_Ar_OHfr_Ar_NHfr_COOfr_COO2fr_C_Ofr_C_O_noCOOfr_C_Sfr_Iminefr_NH1fr_NH2fr_N_Ofr_Ndealkylation1fr_Ndealkylation2fr_Nhpyrrolefr_SHfr_aldehydefr_alkyl_carbamatefr_alkyl_halidefr_allylic_oxidfr_amidefr_amidinefr_anilinefr_aryl_methylfr_azidefr_azofr_barbiturfr_benzenefr_benzodiazepinefr_bicyclicfr_diazofr_dihydropyridinefr_epoxidefr_esterfr_ether
051241000000011210100000000000000000000000000
181780000000000330010000000000301000010100001
277530000000000110000000000000101000010100000
333920000000000110020000000030101000010100001
417610000000000000000001000000000000010000001
560140000000000000000010000000000000020200000
660470000000000110000000000000000000030100002
762880000000200000020000200000000100000000000
810120000000000000000000000000000000020100001
922860000000000000000000000000000200010100001

Last rows

df_indexfr_Al_COOfr_Al_OHfr_Al_OH_noTertfr_ArNfr_Ar_COOfr_HOCCNfr_Ar_OHfr_Ar_NHfr_COOfr_COO2fr_C_Ofr_C_O_noCOOfr_C_Sfr_Iminefr_NH1fr_NH2fr_N_Ofr_Ndealkylation1fr_Ndealkylation2fr_Nhpyrrolefr_SHfr_aldehydefr_alkyl_carbamatefr_alkyl_halidefr_allylic_oxidfr_amidefr_amidinefr_anilinefr_aryl_methylfr_azidefr_azofr_barbiturfr_benzenefr_benzodiazepinefr_bicyclicfr_diazofr_dihydropyridinefr_epoxidefr_esterfr_ether
851246151000100022200010000000000001000010000000
851394340000000000110010000000000101100030000001
851487310000000000110000000000000100000020100002
851520060000000000000000000000000000000010100002
8516111030000000000330010000000000300000020100001
851787690000000100110020000100000101100010100000
851852470002000000000002000000000002000040000000
851938350000001000000000300000000000000010000000
8520109800000000000220000010000030100000000000011
852134730000000000220010000000000101100010000011